Field-deployable concussion detector

ABSTRACT

A method and apparatus for providing an on-site diagnosis of a subject to determine the presence and/or severity of a concussion is provided. The method includes placing an electrode set coupled to a handheld base unit on the subject&#39;s head, acquiring brain electrical signals from the subject through the electrode set, processing the acquired brain electrical signals using a signal processing algorithm stored in a memory of the base unit, determining the presence and/or severity of a concussion from the processed signals, indicating the presence and/or severity of a concussion on the handheld base unit, and determining a course of treatment for the subject based on the indication.

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/195,001, filed on Aug. 2, 2005, which is incorporated hereinby reference.

TECHNICAL FIELD

Embodiments consistent with the present invention relate to the field ofemergency triage, and specifically, a portable apparatus and method forperforming emergency neurological triage on a subject who has recentlysuffered a head injury to determine if the subject has a concussion.

BACKGROUND

Objective and sensitive methods to detect subtle brain dysfunctionresulting from concussion is needed. According to reports from the U.S.Military, blast concussion brain injury is the most significantproportion of current casualties in Iraq and Afghanistan. However,inadequate preparation and clinical tools to recognize and properlytreat such casualties increases the profile of these injuries and theiraftereffects. The brain performs the most complex and essentialprocesses in the human body. Surprisingly, contemporary health carelacks sophisticated tools to objectively assess their function. Apatient's mental and neurological status is typically assessedclinically by an interview and a subjective physical exam. A typicalclinical laboratory currently has no capacity to assess brain functionor pathology, contributing little more than identification of poisons,toxins, or drugs that may have externally impacted the CNS. Theselaboratories can diagnose possible concussions, through the physicalexam, but determining the severity of the concussion cannot be done withany accuracy.

Brain imaging studies, such as computed tomography imaging (CT),magnetic resonance imaging (MRI), are widely used and useful. Thesestructural and anatomical tests, however, reveal little informationabout brain function. The “functional MRI” (fMRI) is a recentimprovement over MRI. fMRI testing provides relative images of theconcentration of oxygenated hemoglobin in various parts of the brain.While the concentration of oxygenated hemoglobin, which shows the usageof oxygen, is a useful indication of the gross metabolic function ofspecific brain regions, it provides very limited or no information aboutthe underlying brain function, i.e., the processing of information bythe brain, which is electrochemical in nature.

For example, an injured brain part can be using a “normal” amount ofoxygen. An fMRI will thus not be able to diagnose a condition or injurywhich may be dramatically dysfunctional. Moreover, in the immediate timefollowing an acute traumatic brain injury (TBI), such as a concussion,CT and MRI/fMRI imaging studies are typically negative, revealing nostructural abnormalities, even when there is clear and dramaticallyabnormal brain function. The same is also true of diffuse axonal injury(DAI), related to shearing of nerve fibers which is present in themajority of concussive brain injury cases, and can remain invisible onmost routine structural images. Swelling or edema from DAI resultingfrom a concussion can subsequently lead to coma and death.

Further, CT and MRI/fMRI testing devices are completely unavailable inportable, field-deployable applications, due to their size, powerrequirements and cost. These assessment tools play an important role inselected cases, but they are costly, not universally available, and theydo not provide critical information at the early stages of acute caresituations. Current technologies are unable to provide the immediate,actionable information critical to timely intervention, appropriatetriage, or the formulation of an appropriate plan of care for acutebrain trauma such as a concussion. However, the brain has the leastcapacity for repair among organs, and thus time sensitive triage andintervention is very important in treating brain injuries such asconcussions.

All of the brain's activity, whether reflexive, automatic, unconscious,or conscious, is electrical in nature. Through a series ofelectro-chemical reactions, mediated by molecules calledneurotransmitters, electrical potentials (voltages) are generated andtransmitted throughout the brain, traveling continuously between andamong the myriad of neurons. This activity establishes the basicelectrical signatures of the electroencephalogram (EEG) and createsidentifiable frequencies which have a basis in anatomic structure andfunction. Understanding these basic rhythms and their significance makesit possible to characterize the EEG as being within or beyond normallimits. At this basic level, the EEG serves as a signature for bothnormal and abnormal brain function.

The electrical activity of the brain has been studied extensively sincethe first recordings over 75 years ago, and especially since the adventof computers. “Normal” electrical activity of the brain has been wellcharacterized in hundreds of studies, with a narrow standard deviation.The frequencies of electrical activity of some parts of the brain arethe normal response to various stimuli, such as acoustic, visual, orpain, known as “evoked potentials.”

Evoked potentials (EP) are particular waves that have characteristicshapes, amplitudes and duration of peaks within those wave shapes, andmany other features, all of which have well established normative data,generated over decades of research. Normative data for all of the EEGand evoked response waves are remarkably constant across differentgenders, ages, and ethnicities. Moreover, any variability that doesexist is well described and explained.

Neuroscientists have also characterized the EEG signature of variousdifferent brain pathologies. Just as an abnormal electrocardiogram (ECG)pattern is a strong indication of a particular heart pathology, anirregular brain wave pattern is a strong indication of a particularbrain pathology such as concussion. A large body of data, withcontinuing refinements and contributions, constitutes the field ofclinical neurophysiology.

Even though EEG-based neurometric technology is accepted today and atremendous body of data exists, application in the clinical environmentis notably limited. Some of the barriers limiting its adoption include:the cost of EEG equipment, its lack of portability, the need for atechnician to administer the test, the time it takes to conduct thetest, and the need for expert interpretation of the raw data. Moreimportantly, the technology is neither available nor practical in theacute care setting, especially at the point of care. A completediagnostic EEG instrument typically costs $80,000, fully equipped.Despite the high costs, the instrument produces essentially rawwaveforms which must be carefully interpreted by an expert. Moreover,use of the standard EEG equipment remains extremely cumbersome. It cantake 30 minutes or more to apply the required 19 electrodes. Once asubject is prepared for the test, the EEG recording can take from 1 to 4hours. Data is collected and analyzed by an EEG technician, and is thenpresented to a neurologist for interpretation and clinical assessment.There are some self-standing dedicated neurodiagnostic laboratorieswhich focus strictly on detailed analysis of electrical brain data.Neither the specialized centers, nor the typically large hospital EEGmachines are practical for the ER, operating room (OR), intensive careunit (ICU), or any other acute care medicine setting where patients arein the greatest need.

Studies conducted by medical professionals worldwide have highlightedthe need for developing a way to provide an early diagnosis andeffective treatment for patients who have suffered a traumatic headinjury, in particular, a concussion. Head injuries, such as aconcussions, may have serious long term effects. For example, one of thelargest threats posed by a concussion is delayed brain swelling causedby fiber shearing, which, if left untreated, can cause coma and death.When properly diagnosed, concussions may be treated using differenttreatment options, but each of the treatments includes its own risk, andshould be administered based on the severity of the injury. Immediate,functional concussion detection is needed to treat patients withpossible concussions for the prevention of further damage anddisability.

Many times, despite the need for early detection, concussions often goundetected, particularly when a subject is not exhibiting any visiblewounds. CT scans and MRI images have less than a 60% accuracy for thedetection of a concussion, and are limited in tracking progress of theconcussion and guiding treatment. Electrical signals emitted by thebrain, however, may be an accurate detector of concussion and its'aftereffects, usually having an accuracy of 90-95%. Moreover, monitoringthe brain's electrical signals may also be used to monitor the progressof the concussion over time allowing for excellent treatment management.

SUMMARY

Consistent with the present invention, there is provided a portabledevice, Brain Concussion Detector using Bx™ technology, for detectingthe presence and/or severity of a concussion in a subject, comprising aheadset comprising a plurality of brain-electrical-signal-detectingelectrodes, and a hand held base unit operably coupled to the headset,the base unit comprising a processor, a memory, the memory containinginstructions for causing the processor to perform a signal processingalgorithm on the detected signals, and an indicator for providing anindication of the presence and/or severity of a concussion.

Consistent with the present invention, there is also provided a methodfor providing an on-site diagnosis of a subject to determine thepresence and/or severity of a concussion, comprising placing anelectrode set coupled to a handheld base unit on the subject's head,acquiring brain electrical signals from the subject through theelectrode set, processing the acquired brain electrical signals using asignal processing algorithm stored in a memory of the base unit,determining the presence and/or severity of a concussion from theprocessed signals, indicating the presence and/or severity of aconcussion on the handheld base unit, and determining a course oftreatment for the subject based on the indication.

Consistent with the present invention, there is also provided a methodfor determining if a subject has suffered a recent concussion using aportable handheld device, comprising acquiring brain electrical signalsfrom the subject using an electrode set operably coupled to the handhelddevice, processing the acquired signals using a signal processingalgorithm, determining if the subject has a concussion using theprocessed signals, and indicating the determination on the portablehandheld device.

Consistent with the present invention, there is also provided a portablehandheld device, Brain Concussion Detector using Bx™ technology, fordetecting the presence and/or severity of a concussion in a subject,comprising a headset comprising a plurality ofbrain-electrical-signal-detecting electrodes and means for evokingneurological potentials, and a handheld base unit operably coupled tothe headset, the base unit comprising a processor, a memory, the memorycontaining instructions for causing the processor to perform a signalprocessing algorithm on the detected signals, a display, the displayproviding a visual display of the presence and/or severity of aconcussion, at least one of a video or audio recording device forrecording at least one of audio or video of the subject, the memorycontaining instructions for causing the processor to analyze therecorded at least one of audio or video and extract features from the atleast one of audio or video recordings, the extracted features beingused by the processor in the signal processing algorithm, and a wirelesscommunication device for transmitting the detected and processed signalsto a remote database.

Further consistent with the present invention, there is provided aportable handheld device, Brain Concussion Detector using Bx™technology, for detecting the presence and/or severity of a concussionin a subject, comprising a headset comprising a plurality ofneurological signal-detecting electrodes and means for evokingneurological potentials, and a handheld base unit operably coupled tothe headset, the base unit comprising a processor, a memory, the memorycontaining instructions for causing the processor to perform a signalprocessing algorithm on the detected signals, and a display, the displayproviding a visual display of the presence and/or severity of aconcussion, wherein the display provides a color-coded indication of thepresence and/or severity of a concussion, the color-coded indicationcomprising a red indication, which is displayed if the concussion ispresent and serious an orange indication, which is displayed if aconcussion is likely present, and more tests are required to beperformed on the subject, and a green indication, which is displayed ifthere is no concussion.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments consistent with thepresent invention and together with the description, serve to explainthe principles of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a portable handheld base unit of a concussion detectiondevice consistent with the present invention.

FIG. 2 shows a schematic diagram of the portable handheld base unitconsistent with the present invention.

FIG. 3 shows a flowchart diagramming the steps of providing an on-sitediagnosis of a patient believed to have a concussion consistent with thepresent invention.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments consistent with thepresent the invention, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

Collected normative data has been used to establish quantitativefeatures of brain electrical activity which clearly distinguish normalbrain function from abnormal dysfunctional conditions. This normativedata has been shown to be independent of racial background and to haveextremely high test-retest reliability, specificity (low false positiverate) and sensitivity (low false negative rate). Conducted studies of15,000 normal and pathological evaluations have demonstrated that brainelectrical signals are highly sensitive to changes in normal brainfunction, and change their characteristics instantaneously aftercatastrophic events such as concussive (blast) or traumatic (impact)brain injuries, ischemia or stroke, and also reflect a variety ofchronic developmental, neurological and psychiatric disorders which arenot related to any detectable change in physical brain structure.Because different types of brain injuries and diseases affect brainelectrical activity in different ways, it is possible to differentiatenot only normal from abnormal function, but also to independentlydetermine which kind of pathology is affecting the brain and to whatdegree, providing guidance on how to restore brain function toward morenormal operation. Embodiments consistent with the present invention usethis as a basis for providing a diagnosis.

Consistent with the present invention, a portable device, BrainConcussion Detector using Bx™ technology, able to detect a concussionfrom the brain's electrical signals includes a portable handheld baseunit, a headset including a plurality of electrodes connected to thehandheld base unit, and software installed on the handheld base unitfor, among other things, processing the detected brain signals anddetermining if a subject has a concussion. FIG. 1 shows the portablehandheld base unit of a concussion detection device consistent with thepresent invention. The portable handheld base unit 100 consistent withthe present invention includes a navigation pad 102, which may include aplurality of navigation buttons and a selection button, allowing a userto navigate through menus illustrated on a screen 104, and selectoptions presented on screen 104. Consistent with the present invention,screen 104 may comprise an LCD, LED, OLED, or plasma screen. Screen 104may also comprise simple LED (or other illumination means) indicators,which provide an indication of, for example, whether the device is on,if tests are being performed, or the presence and/or severity of aconcussion.

Consistent with one embodiment of the present invention, the presenceand/or severity of a concussion may be indicated using a color-codedlight source. A red light source could be illuminated if a concussion ispresent and serious, an orange light source could be illuminated if aconcussion is likely present, but requires more tests to be performed onthe subject, and a green light source could be illuminated if there isno concussion. The color-coded indication provides a simple, easy-to-useand easy-to-read means for quickly determining and diagnosing thepresence of a concussion in a subject.

Consistent with the present invention, navigation pad 102 may be used toselect and execute functions to be performed by handheld base unit 100.For example, screen 104 may display a menu of options highlightingpossible options for performing tests on a subject. These options mayinclude beginning testing, selecting the types of tests to perform,and/or options for processing or transmitting acquired data.

Navigation pad 102 may also be used to enter additional informationconcerning a subject who has suffered a head injury. This additionalinformation includes standard information observed about a subject whohas suffered a head injury, including whether the subject is dizzy orunsteady, whether the subject is nauseous or vomiting, whether thesubject is unresponsive, and information acquired from the subject inresponse to interview questions, which could reveal such information asmemory loss, loss of vision, ringing in the ears, confusion, orheadache. Consistent with an embodiment of the present invention,software stored in a memory of handheld base unit 100 could display onscreen 104 a selection screen allowing a user to select the subject'sobservable features, and information acquired through the interview.Such a selection screen may take the form of a list showing commonnoticeable features of a concussion, allowing a user to select via acheckbox the observed features.

Handheld base unit 100 may be coupled to a headset (not shown) includinga plurality of electrodes via connecting means 106. Connecting means 106may include a permanently attached or detachable cable or wire, or mayinclude a wireless transceiver, capable of wirelessly transmitting andreceiving signals from the headset.

Handheld base unit 100 may also include transceiving antenna 108.Consistent with an embodiment of the present invention, transceivingantenna 108 may be used to wirelessly transmit data stored in thehandheld base unit 100 to a remote location for storage or furtherprocessing. This data may include diagnosis data, treatment data, or rawelectrical signals. The remote location may be a personal computer or alarge database. A personal computer may be used for storing and furtherprocessing acquired data, allowing, for example, a medical professionalto monitor the progress of a subject through the treatment of aconcussion. A remote database may be used for storing the acquired data,to allow the acquired data to be added to a larger data pool of subjectshaving similar brain electrical signals. This larger data pool may beused for neurometric studies to provide a more accurate diagnosis on thebasis of comparison.

Handheld base unit 100 may further include an audio/visual datareceiving means 110. Audio/visual receiving means 110 may comprise acamera (still/video, or both) and/or a microphone. Consistent with thepresent invention, in addition to obtaining brain electrical signalsthrough the headset, additional data about the subject may be acquiredusing audio/visual receiving means 110. This data may include video datashowing the subject's facial expressions, eye movement, and balance,and/or audio data including the subject's responses to questions givenduring a post-injury interview exam revealing a subject's slurred speechor loss of memory. Specific, recognizable features to be extracted fromthis additional audio/visual data may be used in conjunction withacquired brain electrical signals to provide a diagnosis of a subject,and determine if the subject suffered a concussion.

Further consistent with the present invention, software stored in amemory of handheld base unit 100 may be configured to display on screen104 results of the testing. Results may include displaying a brain mapgenerated from the acquired data showing an indication of a braininjury, a location of a brain injury, or a severity of a brain injury.Results may also include a simple indication of a concussion. The simpleindication may comprise a red/orange/green light source as describedabove, or may be a simple text display indicating the presence and/orseverity of a concussion.

Software stored in a memory of handheld base unit 100 may further beconfigured to display on screen 104 additional information related tothe testing of a subject or the operation of the device. For example,memory may contain interactive instructions for using and operating thedevice to be displayed on screen 104. The interactive instructions maycomprise a feature-rich presentation including a multimedia audio/videorecording providing visual and audio instructions for operating thedevice, or may simple be a text file, displayed on screen 104,illustrating step-by-step instructions for operating and using thedevice. The inclusion of interactive instructions with the deviceeliminates the need for a device that requires extensive training touse, allowing for deployment and use by non-medical professionals.

FIG. 2 shows a schematic diagram of portable handheld base unit 100consistent with the present invention. As shown in FIG. 2, handheld baseunit 100 is connected to headset 200. Headset 200 may include anelectrode set 202 for detecting brain electrical signals to be placed ona subject's head 204. Electrode set 202 may comprise a reduced electrodeset, having less than 19 electrodes, and preferably less than 10electrodes. Headset 200 may also include a stimulus emitter 206 to beused for evoked potential tests. Stimulus emitter 206 may include anaudio or visual stimulus emitter.

Handheld base unit 100 also includes an electronics block 208 includingprocessor 210, memory 212, and a power source 214 for providing power tothe electronics block. In one embodiment consistent with the presentinvention, power source 214 comprises a rechargeable battery, which canbe recharged when coupled to a charger 216 being powered by an AC or DCpower source 218.

Electronics block 208 is further coupled to headset 200, user interfaceelectronics 220 for controlling, for example, navigational pad 102,display electronics 222 for controlling, for example, screen 104, andconsistent with an embodiment of the present invention, wirelesselectronics 224 for controlling, for example, wireless transceiver 108and/or a wireless connection 106 to headset 200. Electronics block 208is also coupled additional A/V electronics 226 for controlling, forexample, A/V receiving means 110. In general, memory 212 containsinstructions for causing processor 210 to perform functions foroperating portable handheld device 100, including all of the electronicsillustrated in FIG. 2, and for performing tests on a subject andproviding a diagnosis based on the performed tests, as will be describedin greater detail.

FIG. 3 shows a flowchart diagramming the steps of providing an on-sitediagnosis of a patient believed to have a concussion consistent with thepresent invention, and will be described in conjunction with FIG. 2 toillustrate a method for providing a diagnosis consistent with anembodiment of the present invention. Electrodes 202 are first placed onthe head of a subject 204 who has just received a head injury and mayhave a concussion (step 302). Handheld base unit 100 is powered on usingpower supplied from battery 214, and processor 210 executes instructionsstored in memory for controlling display electronics 222 to displayinformation including a power state, a readiness state, a testing mode,and/or a message for the user to enter a command. A user then usesnavigation pad 102 to enter a command to start the testing. If the userdetermines that evoked potentials may also have to be recorded (step301), he may initiate stimulus emitter 206 and apply stimuli to thebrain to elicit evoked potentials (step 303). User interface electronicspasses the user command to electronics block 208, and processor 210interprets the command and provides a signal to headset electronics tobegin acquiring signals. Brain electrical signals, which may include atleast one of spontaneous or evoked potentials are acquired from headsetelectrodes 202 (step 304 or step 305) and passed from headsetelectronics to electronics block 208 for processing. Processor 210 thenexecutes instructions contained in memory 212 for processing theacquired signals (step 306).

In an embodiment consistent with the present invention, the signals areprocessed to remove noise, processed to extract features, and processedto classify the extracted features. More specifically, memory 212 cancontain instructions that are executed by the processor 210 which forprocessing the signals using a Dual-Tree Complex Wavelet Transform as aninvertible transform to adaptively filter the acquired signals. Theinstructions further may contain an implementation of an algorithmcarried out by processor 210, wherein a complex wavelet transform iscomputed for each sub-average, and then the phase variance of eachnormalized wavelet coefficient w_(i,j) is computed. The magnitude ofeach wavelet coefficient is selectively scaled according to the phasevariance of the coefficients at this location across the sub-averages.The scaling has the form:

w _(i,j)=α_(i,j) W _(i,j)exp(jθ _(i,j)),

where W_(i,j) and θ_(i,j) are respectively the magnitude and phase ofthe unprocessed complex i^(th) wavelet coefficient at the j^(th) scale,and where:

α_(i,j)=exp(−0.75(F _(ij) /T _(max))⁴,

where F_(ij) is the phase variance of coefficient w_(ij) across thesub-averages. The filtered signals are averaged and an automatic peakdetection algorithm is implemented by processor 210 to determine thefollowing peak locations and latencies: Peak 1, Peak 2, and Interpeak1-5 latency. These values are then compared by processor 210 tonormative data contained in memory 212.

In an embodiment consistent with the present invention, processing thesignals may comprise performing an algorithm for removing noise from theacquired signals, or “denoising.” In one embodiment, the denoisingalgorithm utilizes wavelet-based signal processing using wavelettransforms. In other embodiments, the algorithm may comprise a diffusiongeometry processing algorithm or a fractal processing algorithm. Thewavelet transform, a member of the family of Fourier transforms, is aprocess of decomposing a given signal into a set of orthonormal basisfunctions called wavelets. In traditional discrete Fourier transform(DFT), a signal is decomposed using complex sinusoids as basisfunctions, producing a frequency domain representation of the signal. Incontrast, a discrete wavelet transform (DWT) uses a family ofspecifically designed wavelets, or little waves, as basis functions. Afamily of wavelets is created by dilating the original wavelet function,termed the “mother wavelet.” A wavelet transform decomposes the signalin both time and frequency using different dilations of the motherwavelet. With the application of DWT, the one dimensional finite signalx[n] is represented in two-dimensional “wavelet coordinates.” Individuallevels of signal decomposition are created, called scales. At each scalea set of coefficients is created by computing the inner product of theoriginal signal x[n] with a scaled version of the mother wavelet. Themother wavelet function is designated by T, and its dilations aredesignated by Ψ(j). The position index of a wavelet at scale j is calleda translation. The value of the wavelet is completely described by thetwo dimensional sequence Ψ(j,k), where j is the scale index of thewavelet, and k is the translation index. The DWT is the defined as:

${{C\left( {j,k} \right)} = {\sum\limits_{n = 0}^{N - 1}{{x\lbrack n\rbrack}{\Psi_{j,k}\lbrack n\rbrack}}}},{{{where}\mspace{14mu} {\Psi_{j,k}\lbrack n\rbrack}} = {2^{\frac{- j}{2}}{\Psi \left( {{2^{- j}n} - k} \right)}}}$

Coefficients C(j,k) are the wavelet coefficients at different scales jand translations k of the inner product of the wavelet Y(j,k) with theoriginal signal x[n]. In wavelet coordinates, information about both thefrequency and the location (time) of the signal energy is preserved.This is a process of noise suppression that utilizes assumptions aboutsmoothness and coherence properties of both the underlying signal andthe noise that contaminates it. Similar to filtering in the frequencydomain, the wavelet coefficient thresholding algorithm reduces sets ofwavelet coefficients in the wavelet domain. This process is based on theassumption that the underlying signal is smooth and coherent, while thenoise that is mixed with the signal is rough and incoherent. Smoothnessof a signal is a property related to its bandwidth, and is defined inrelation to how many times a signal can be differentiated. The degree ofsmoothness is equal to the number of continuous derivatives that can becalculated. A signal is coherent if its energy is concentrated in bothtime and frequency domains. An incoherent noise is “spread out,” and notconcentrated. One measure of coherence is how many wavelet coefficientsare required to represent 99% of the signal energy. A time-frequencysignal space is completely spanned by wavelet coefficients at all scalesand translations. A well-concentrated signal decomposition in anappropriately selected wavelet basis will require very few coefficientsto represent 99% of signal energy. However, a completely incoherentnoise will require 99% of the coefficients that span the entire space torepresent 99% of its energy.

This conventional wavelet denoising process is a three step process:

-   -   1. Wavelet transform the signal to obtain wavelet coefficients        at different scales    -   2. Threshold the coefficients and set to zero any smaller than a        threshold δ    -   3. Perform the inverse wavelet transform to approximate the        original signal

In the denoising process, the noise components of the signal areattenuated by selectively setting the wavelet coefficients to zero.Denoising is thus a non-linear operation, because different coefficientsare affected differently by the thresholding function. There are manyparameters to control in this algorithm: level of wavelet decomposition,threshold selection, using different thresholds at different waveletcoefficients that are kept by a fixed amount.

Consistent with an embodiment of the present invention, the denoisingprocess involves dividing the acquired signals into discrete intervals,or “frames,” and then averaging the frames, and denoising the averagedframes. The greater amount of frames that are denoised prior torecomposing the signal, the better the results of the denoising process.Preferably, the frames are combined by using two adjacent frames andcalculating their linear average. This method is chosen for itssimplicity, computational stability, and well-understood behavior. Thisdyadic linear average is then denoised, and a new frame is created. Theoverall idea is to generate as many permutations of the originalarrangement of frames as possible, and keep averaging and denoisingthose new combinations of frames. This recombination process is atree-like process, and may comprise the dual-tree process describedabove, in which new levels of recombined frames are created. The averageand denoise operation creates frames at level k, which are no longer alinear combination of frames from level k−1.

The many possible algorithms to accomplish this task can be evaluated bydifferent criteria: ease of implementation, computational efficiency,computational stability, etc. For the present invention, ease ofimplementation is used, because the key aspect of the invention isimplementation of different wavelet denoising techniques and notcombinatorics of frame rearrangements. The goal of the preferredembodiment in frame rearranging is to produce enough new frames toobtain acceptable performance.

Processor 210 is further configured to execute instructions contained inmemory 212 to perform an algorithm for extracting signals from processedsignals to evaluate the processed signals (step 308). In one embodiment,processor 210 executes instructions which performs a feature extractionalgorithm on the processed signals according to a method disclosed inU.S. Pat. Nos. 6,358,486, 6,052,619 and 5,287,859, which areincorporated herein by reference. The algorithm utilizes Fast FourierTransform (FFT) Analysis is applied to characterize the frequencycomposition of the processed signals, typically dividing the signalsinto the traditional frequency bands: delta (1.5-3.5 Hz), theta (3.5-7.5Hz), alpha (7.5-12.5 Hz), beta (12.5-25 Hz), and gamma (25-50 Hz).Higher EEG frequencies, up to and beyond 1000 Hz may also be used. Thesefeatures can include characteristics of the processed signals such asabsolute and relative power, symmetry, and coherence. In the context ofanalyzing process brainwaves, absolute power is the average amount ofpower in each frequency band and in the total frequency spectrum of theprocessed signals, and is a measure of the strength of the brain'selectrical activity. Relative power is the percentage of the total powercontributed for a respective electrode and a respective frequency bandand is a measure of how brain activity is distributed. Symmetry is theratio of levels of activity between corresponding regions of the twobrain hemispheres in each frequency band and is a measure of the balanceof the observed activity. Coherence is the degree of synchronization ofelectrical events in corresponding regions of the two hemispheres and isa measure of the coordination of the brain activity. These four basiccategories of univariate features, resulting from the spectral analysisof the process signals, are believed to characterize independent aspectsof brain activity and each is believed to be sensitive to a variety ofdifferent clinical conditions and changes of state. A full set ofindividual and pairwise features is calculated and transformed forGaussianity using, for example, the log function. Once a Gaussiandistribution has been demonstrated and age regression applied,statistical Z transformation is performed. The Z-transform is used todescribe the deviations from age expected normal values:

$\begin{matrix}{Z = {{Probability}\mspace{14mu} {that}\mspace{14mu} {subject}\mspace{14mu} {value}\mspace{14mu} {lies}\mspace{14mu} {within}\mspace{14mu} {the}\mspace{14mu} {normal}\mspace{14mu} {range}}} \\{Z = \frac{{{Subject}\mspace{14mu} {Value}} - {{Norm}\mspace{14mu} {for}\mspace{14mu} {Age}}}{{Standard}\mspace{14mu} {Deviation}\mspace{14mu} {for}\mspace{14mu} {Age}}}\end{matrix}$

The significance of the Z-transform is that it allows measures withdifferent metrics to be combined using the common metric of probability.Using a database of acquired brain electrical signals from a largepopulation of subjects believed to be normal, or to have otherconditions, the distribution of these response signals is determined foreach electrode in electrode set 202. In particular, each extractedfeature or factor score is converted to a Z-transform score, or factorZ-score which characterizes the probability that the extracted featurevalue or factor score observed in the subject will conform to a normalvalue.

Processor 210 is further configured to perform an algorithm wherein theextracted features, or the Z-scores are classified to determine thepresence and/or severity of a concussion (step 310). In one embodiment,these sets of univariate data is subjected to Gaussian normalization inorder to improve the accuracy of any subsequent statistical analysis.The Z-scores are given a selected discriminant score. Each discriminantscore is a respective weighted combination of a selected subset ofZ-scores for monopolar and/or bipolar univariate and multivariatefeatures derived from the processed signals of a subject. The processor210 executes an algorithm wherein a respective discriminant score isevaluated for each of two or more diagnostic categories multiplying eachof several selected Z-scores by a respective coefficient and adding theresulting products. The coefficients typically differ as betweendiagnostic categories and as between Z-scores. The probability isevaluated that the subject belongs to one of the two or more diagnosticcategories through a probability evaluating expression which is afunction of the relevant discriminant scores, matching results againstlimits provided by memory 212 for the presence and/or severity of aconcussion. The diagnostic categories may be indicative of whether asubject has a concussion, the severity of the concussion, and whether ornot the concussion requires immediate medical attention.

Consistent with the present invention, a user may also acquireadditional data from the subject (step 312). As further discussed above,such additional data may include video data acquired by A/V electronics226 showing the subject's facial expressions, eye movement, and balance,and/or audio data including the subject's responses to questions givenduring a post-injury interview exam revealing a subject's slurred speechor loss of memory. At the time of enrollment, information may also becollected from the subject using a symptom checklist, and a healthquestionnaire about concussion history, severity and frequency ofpreviously diagnosed concussions. As known in the prior art, the symptomchecklist can be used to rate the severity of each symptom for thecurrent as well as previously sustained head injuries, and the severityratings can be summed to provide an overall grade for each concussionsuffered. This information about concussion grade and the frequency ofconcussion may be entered into the processor 210 by the user. AConcussion Index can then be generated by the processor 210 bymultiplying the grade of each concussion by the frequency of that typeof concussion, and in the case of multiple concussions of differentgrades, summing these products together. A Concussion Index for eachconcussed patient may be stored in the memory 212 and may also bewirelessly transmitted to a remote database to serve as an electronicrecord of the injury. This additional data can be processed by processor210, along with the A/V data (step 314) and used in conjunction with theprocessed acquired brain electrical signals to provide an evaluation ofthe acquired signals (step 308) and determine the presence and/orseverity of the head injury (step 310).

Following the determination of the presence and/or severity of a headinjury, processor 210 executes instructions to provide an indication ofthe presence and/or severity of a head injury (step 316) to be displayedby display electronics 222. The indication may comprise a color-codedindication, a brain map, or a simple message displayed on screen 104, asfurther described above. Processor 210 may then execute an algorithm fordetermining a course of treatment based on the indication, the processedsignals and Concussion Index stored in memory 212 (step 318). Forexample, using the classification techniques described above, processor210 may compare the specific indication, the Concussion Index andassociated acquired signals to data stored in memory, the data furtherindicating treatments applied and its success thereof. The stored datamay further include information relating to the progression of aspecific subject's concussion over time, and the effectiveness ofcertain treatments applied at a time interval. In executing thealgorithm for determining a course of treatment, processor 210 may takeall of this information into account in order to narrowly tailor acourse of treatment for the subject based on the subject's brainsignals. The subject then may be treated (step 320).

Embodiments consistent with the present invention, using advanced signalprocessing algorithms and stored data of the brain electrical signals ofthousands of subjects having concussions and similar brain injuries, mayprovide a rapid and accurate indication if a subject has a concussion.Moreover, the advanced signal processing algorithms may be executed by aprocessor capable of integration in a portable handheld device. Theportable handheld device used with a reduced electrode set allows for arapid, portable solution for determining if a subject has a concussion,and determining a course of treatment, and may enable for the earlyindication of severe concussions, allowing treatment to be given at anearly stage of injury.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-28. (canceled)
 29. A method for determining if a subject has sufferedtraumatic brain injury using a portable handheld device, comprising:acquiring brain electrical signals from the subject using an electrodeset operably coupled to the handheld device; processing the acquiredsignals using, at least in part, a non-linear signal processingalgorithm; determining if the subject has traumatic brain injury usingthe processed signals; determining the severity of traumatic braininjury using the processed signals; and indicating the determination andthe severity on the portable handheld device, wherein processing theacquired signals includes extracting a plurality of features of theacquired signals.
 30. The method of claim 29, wherein extracting aplurality of features comprises extracting a plurality of componentsfrom the acquired signals and, further extracting the distribution ofabsolute power, relative power, symmetry, or coherence among theplurality of components.
 31. The method of claim 30, wherein theplurality of components are a plurality of frequency components.
 32. Themethod of claim 29, wherein the traumatic brain injury is a concussion.33. The method of claim 29, further comprising the step of non-linearclassification of the extracted features.
 34. The method of claim 29,further comprising the steps of calculating Z-scores based on acquireddata, and performing non-linear classification on said Z-scores.
 35. Aportable device for detecting the presence and severity of traumaticbrain injury in a subject, comprising: a headset comprising a pluralityof brain-electrical-signal-detecting electrodes; and a portable baseunit operably coupled to the headset, the base unit comprising: aprocessor; a memory, the memory containing instructions for causing theprocessor to perform, at least in part, a non-linear signal processingalgorithm on the detected signals; and an indicator for providing anindication of the presence and severity of traumatic brain injury. 36.The portable device according to claim 35, wherein the detected signalscomprise at least one of spontaneous or evoked signals.
 37. The portabledevice according to claim 35, wherein the indication of presence andseverity of traumatic brain injury includes the step of evaluatingprocessed signals for each of two or more diagnostic categories.
 38. Theportable device according to claim 35, wherein the non-linear signalprocessing algorithm denoises the detected signals; extracts featuresfrom the denoised signals; builds discriminant functions for classifyingthe extracted features; and detects the presence and severity oftraumatic brain injury based on the classified features.
 39. Theportable device according to claim 38, wherein the non-linear signalprocessing algorithm comprises at least one of a wavelet, a waveletpacket processing algorithm, a diffusion geometry processing algorithm,or a fractal processing algorithm.
 40. The portable device according toclaim 35, wherein the indicator comprises a visual display.
 41. Theportable device according to claim 40, wherein the visual displaycomprises: a first indicator, which is displayed if traumatic braininjury is determined to be present and serious; a second indication,which is displayed if traumatic brain injury is determined to be likelypresent, and more tests are required to be performed on the subject; anda third indication, which is displayed if there is no traumatic braininjury detected.
 42. A method for providing an on-site diagnosis of asubject to determine the presence and severity of traumatic braininjury, comprising: placing an electrode set coupled to a handheld baseunit on the subject's head; acquiring brain electrical signals from thesubject through the electrode set; processing, by a processor in thebase unit, the acquired brain electrical signals using, at least inpart, a non-linear signal processing algorithm stored in a memory of thebase unit; determining the presence and severity of a traumatic braininjury from the processed signals; indicating the presence and severityof traumatic brain injury on the handheld base unit; and determining acourse of treatment for the subject based on the indication.
 43. Themethod according to claim 42, further comprising: storing the acquiredbrain electrical signals; and wirelessly transmitting the stored signalsto a remote database.
 44. The portable device according to claim 42,wherein the indication of presence and severity of traumatic braininjury includes the step of evaluating processed signals for each of twoor more diagnostic categories.
 45. The method according to claim 42,wherein determining the presence and severity of a traumatic braininjury further comprises performing additional tests on the subject. 46.The method according to claim 42, wherein acquiring brain signalscomprises acquiring at least one of spontaneous or evoked signals. 47.The method according to claim 42, wherein processing the acquiredsignals comprises: denoising the acquired signals; extracting desiredfeatures from the denoised signals; building discriminant functions forclassifying the extracted features; classifying the extracted features;and indicating the presence and severity of traumatic brain injury basedon the classified features.
 48. The method according to claim 42,wherein processing the acquired signals using the non-linear signalprocessing algorithm comprises performing at least one of a wavelet, awavelet packet processing algorithm, a diffusion geometry processingalgorithm, or a fractal processing algorithm on the acquired signals.49. The method according to claim 42, wherein indicating the presenceand severity of traumatic brain injury on the handheld base unitcomprises: displaying an indication of traumatic brain injury;displaying the severity of traumatic brain injury; and displaying alocation of the traumatic brain injury.
 50. The method according toclaim 42, further comprising determining the presence and severity oftraumatic brain injury over time, indicating the presence and severityof traumatic brain injury on the handheld base unit over time, and thendetermining a course of treatment for the subject based on a change inthe indication over time.
 51. A method for determining if a subject hassuffered traumatic brain injury using a portable handheld device,comprising: acquiring brain electrical signals from the subject using anelectrode set operably coupled to the handheld device; processing theacquired signals using, at least in part, a non-linear signal processingalgorithm; determining if the subject has traumatic brain injury usingthe processed signals; determining the severity of the traumatic braininjury using the processed signals; and indicating the determination andthe severity on the portable handheld device.
 52. A portable handhelddevice for detecting the presence and severity of traumatic brain injuryin a subject, comprising: a headset comprising a plurality ofneurological signal-detecting electrodes and means for evokingneurological potentials; and a handheld base unit operably coupled tothe headset, the base unit comprising: a processor; a memory, the memorycontaining instructions for causing the processor to perform, at leastin part, a non-linear signal processing algorithm on the detectedsignals; and a display, the display providing a visual display of thepresence and severity of a traumatic brain injury comprising: a firstindication, which is displayed if traumatic brain injury is determinedto be present and serious; an second indication, which is displayed iftraumatic brain injury is determined to be likely present, and moretests are required to be performed on the subject; and a thirdindication, which is displayed if there is no traumatic brain injurydetermined to be present.